Income. Income Amounts. Income Segments. As part of the Core survey, GWI asks all respondents about their annual household income.

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1 Income Amounts Income Segments As part of the Core survey, GWI asks all respondents about their annual household income. We state that they should think about their household income, rather than their individual personal income; this is to ensure we get a complete picture of their circumstances, which is not always the case when you ask about personal income only (especially for those people who might not be employed, but have a partner who is). We also remind respondents that they should think about an annual, rather than monthly, figure. This is to ensure we capture comparable data across all markets, despite some countries being more accustomed to thinking about monthly earnings. In each country, we give respondents a range of household income brackets to choose from; these are tailored to each market to reflect the reality that average household incomes differ between countries. Until Q3 2016, this question had a free-text box where people entered an exact income figure. However, we decided to change to the current system of pre-defined brackets for two key reasons: Some respondents were reluctant to give an exact figure, which led to relatively high results for Prefer not to say or Don t know. Once we changed to the new approach, we saw a notable increase in the number of people giving an answer. Frequently, respondents would shorten their answers e.g. by writing 30 instead of 30,000; in many instances, it was clear how such an answer should be corrected, but there were other cases where we were having to discount the figure entirely. Similarly, in countries where currencies have high denominations and many people could have income amounts stretching to tens of millions, it was relatively cumbersome for them to enter their answer. This was exacerbated further by the tendency to shorten numbers, described above. In each country, the income amounts are given in the respondent s local currency. We are investigating adding a new element to PRO Platform where these figures are converted to USD and hope to have this in place by the end of 2018; we can t ask respondents to answer in USD as few of them would know an accurate exchange rate, and asking them to make a conversion could lead to mistakes. In some markets in LatAm and the Middle East, we were aware that respondents were still entering monthly figures despite us adding clarification text in 2016; throughout 2018, the question format in these countries is therefore being revised. In each country, we use the results from the household income question to determine income segments dividing people into the Lower Quartile / Bottom 25%, the Middle Quartile / Mid 50% or the Upper Quartile / Top 25%. We also break out the Top 10% (who still appear within the Top 25% too). These segments exclude anyone who answered Don t know or Prefer not to say in the income question; these individuals are instead included in the Prefer not to say segment. To allow a way to analyze these respondents alongside those who did give an income answer, we have developed a separate social grading classification which factors in much more in addition to income. You can find details of this here. To assign respondents to an income quartile, we look at the spread of responses received in that country in that quarter s wave of research. There are two key things to note about this: Respondents are assigned to a segment based on how their answer compares to everyone else we surveyed in their country only, and not against any respondents in any other markets. Because average incomes can differ so much between countries, that means the thresholds of each segment are likely to be different from one country to the next; in a market like the US where average incomes are among the highest globally, the incomes of those in the top 25% are likely to be considerably greater than those in markets with low average incomes (e.g. African markets like Kenya and Nigeria). This has an impact on how the data should be interpreted when analyzed at a global level. Rather than showing the top 25% of global respondents which would cause a huge skew towards wealthy markets it displays aggregated data for the top 25% from each country. As the segments are created after each wave of research and are based on the answers captured that quarter, the thresholds for each segment might be subject to wave-on-wave variation. In the US, for example, it might be that those with an income of less than 25,000 USD comprise the bottom 25% in one wave, whereas in the subsequent wave it comprises those with an income of less than 32,000 USD. In practice, it tends to be the same brackets that contribute to the same segments each quarter, but you can double check this by applying the income segments as audiences on the annual household income question. As a result of asking people to select an income bracket rather than entering an exact amount, it is not currently possible to give a precise value for the threshold of each income segment. However, we can determine the income brackets that normally contribute to each segment by looking at the data from recent waves: 1

2 Argentina Argentine Peso ARS 108, , , , , , , , , , , , , , ,000 Australia Australian Dollar AUD 30,001 42,000 30,001 42,000 42,001 50,000 50,001 65,000 65,001 75,000 75,001 90,000 90, , , , , , , , , ,000 Austria 5,000 5,001 10,000 Belgium 5,000 5,001 15,000 30,001 36,000 36,001 42,000 42,001 50,000 42,001 50,000 72, , , , ,001 72, , , , ,001 Brazil Brazilian Real BRL 10,001 25,000 10,001 25,000 25,001 38,000 38,001 50,000 90, , ,001 90, , ,0001 Canada Canadian Dollar CAD 60,001 70,000 70,001 80, , , , , , , , , ,000 China Chinese Yuan CNY 20,001 55, , , , , , , , , , ,000 Colombia Colombian Peso COP 4,500,000 4,500,001 11,000,000 4,500,001 11,000,000 11,000,001 16,000,000 16,000,001 to 20,000,000 20,000, ,000,000 20,000,000 Denmark Danish Krone DKK 150, , , , , , , , , , , , , , , ,000-1,000, ,000-1,000,000 Egypt Egyptian Pound EGP 30,000 30,000 30,001 45,000 45,001 60,000 60,001 80, , ,

3 France 2,000 2,001 10,000 10,001 17,000 10,001 17,000 17,001 22,000 22,001 27,000 27,001 30,000 30,001 37,000 37,001 45,000 37,001 45,000 55,001 90,000 55,001 90,000 Germany 2,500 2,501 10,000 Ghana Ghanaian Cedi GHS 4,000 4,000 4,001 8,000 8,000 12,000 12,001 16,000 16,001 20,000 16,001 20,000 50,001 75, ,001 50,001 75, ,001 Hong Kong Hong Kong Dollar HKD 80,000 80, , , , , , , , , , , , , , , , ,001 1,250,000 1,250,001 1,500, ,001 1,250,000 1,250,001 1,500,000 India Indian Rupee INR 50,000 50, , , , , , , , , , , , , , , , ,001 1,000, ,001 1,000,000 2,000,001 2,000,001 Indonesia Indonesian Rupiah IDR 3,000,000 3,000,001 7,000,000 3,000,001 7,000,000 7,000,001 20,000,000 20,000,001 35,000,000 35,000,001 50,000,000 50,000,001 60,000,000 60,000,001 85,000,000 85,000, ,000,000 85,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000,001 Ireland 30,001 38,000 38,001 45,000 45,001 50,000 50,001 65,000 65,001 80,000 65,001 80,000 Italy 10,001 15,000 40,001 45,000 45,001 60,000 60,001 90,000 45,001 60,000 60,001 90,000 Japan Japanese Yen JPY 1,200,000 1,200,001 3,000,000 3,000,001 3,800,000 3,000,001 3,800,000 3,800,001 4,500,000 4,500,001 5,000,000 5,000,001 6,000,000 6,000,001 7,000,000 7,000,001 8,500,000 7,000,001 8,500,000 8,500,000 10,000,000 10,000,001 15,000,000 15,000,000 20,000,000 20,000,001 10,000,001 15,000,000 15,000,000 20,000,000 20,000,

4 Kenya Kenyan Shilling KES 60,000 60,000 60, , , , , , , , , , , , , , , ,000 1,200,001 1,800,000 1,800,001 2,400,000 2,400, , ,000 1,200,001 1,800,000 1,800,001 2,400,000 2,400,001 Malaysia Malaysian Ringgit MYR 9,000 9,001 20,000 40,001 60,000 60,001 80, , , , , , ,000 Mexico Mexican Peso MXN 48,000 48,001 96,000 48,001 96,000 96, , , , , , , , , , , ,001 Morocco Moroccan Dirham MAD 6,000 6,000 6,001 12,000 12,001 18,000 18,001 24,000 24,001 30,000 30,001 36,000 36,001 48,000 36,001 48,000 48,001 60,000 60,001 72,000 72,001 84,000 84,001 96,000 96,001 72,001 84,000 84,001 96,000 96,001 Netherlands 14,500 14,501 20,000 30,001 35,000 35,001 40,000 40,001 45,000 55,001 65,000 65, ,000 65, ,000 New Zealand New Zealand Dollar NZD 50,001 70,000 70,001 90, , , , , , , , , , , , ,000 Nigeria Nigerian Naira NGN 100, , , , , , , , , , , , ,001 1,000, ,001 1,000,000 1,000,001 2,000,000 2,000,001 4,000,000 4,000,001 6,000,000 6,000,001 8,000,000 8,000,001 2,000,001 4,000,000 4,000,001 6,000,000 6,000,001 8,000,000 8,000,001 Philippines Philippine Peso PHP 20,001 50,000 20,001 50,000 50, , , , , , , , , , , , , ,000 1,000,001 1,250,000 1,250, , ,000 1,000,001 1,250,000 1,250,001 Poland Polish Zloty PLN 40,001 45,000 45,001 50,000 60,001 70,000 70,001 80,000 80, ,000 80, , , , , , , , , , , ,

5 Portugal 2,500 2,501 8,000 8,001 12,000 8,001 12,000 12,001 15,000 15,001 18,000 18,001 20,000 20,001 24,000 24,001 30,000 24,001 30,000 30,001 38,000 38,001 60,000 75,001 38,001 60,000 75,001 Russia Russian Ruble RUB 30,000 30,001 60,000 60, ,000 60, , , , , , , , , ,000 1,200,000 1,400,000 1,400,001 1,600,000 1,600,000 1,200,000 1,400,000 1,400,001 1,600,000 1,600,000 Saudi Arabia Saudi Arabian Riyal SAR 12,000 20,001 40,000 40,001 60,000 60,001 90,000 90, , , , , , , , , , , , , ,000 Singapore Singapore Dollar SGD 4,000 4,000 8,000 8,001 25,000 8,001 25,000 25,001 40,000 40,001 55,000 55,001 70,000 70,001 85,000 85, , , , , , , , , , , ,001 South Africa South African Rand ZAR 9,000 9,001 15,000 15,001 30,000 15,001 30,000 30,001 60,000 60, , , , , , , , , , , , , , ,001 1,000, , , ,001 1,000,000 South Korea South Korean Won KRW 4,000,000 4,000,001 20,000,000 20,000,001 30,000,000 20,000,001 30,000,000 30,000,001 38,000,000 38,000,001 45,000,000 45,000,001 50,000,000 50,000,001 58,000,000 58,00,001 65,000,000 65,000,001 80,000,000 65,000,001 80,000,000 80,000, ,000, ,000, ,000, ,000,001 80,000, ,000, ,000, ,000, ,000,001 Spain 2,000 2,001 10,000 10,001 15,000 20,001 22,000 22,001 27,000 27,001 31,000 31,001 40,000 31,001 40,000 Sweden Swedish Krona SEK 40,000 40, , , , , , , , , , , , , , , , , ,000 Switzerland Swiss Franc CHF 45,000 45,001 65,000 45,001 65,000 65,001 85, , , , , , ,

6 Taiwan New Taiwan Dollar TWD 150, , , , , , , , , , , ,001 1,000,000 1,000,001 1,200,000 1,200,001 1,500,000 1,200,001 1,500,000 2,000,001 2,500,000 2,500,001 3,000,000 3,000,001 2,000,001 2,500,000 2,500,001 3,000,000 3,000,001 Thailand Thai Baht THB 60,000 60, ,000 60, , , , , , , , , , , , , , ,001 1,100,000 1,100,001 1,500, ,001 1,100,000 1,100,001 1,500,000 Turkey Turkish Lira TRY 2,000 2,001 4,000 4,001 12,000 30,001 35,000 35,001 45,000 UAE UAE Dirham AED 20,001 40,000 20,001 40,000 40,001 65,000 65, , , , , , , , , , , , , , , ,001 UK Pound Sterling GBP 10,001 15,000 20,001 24,000 24,001 29,000 29,001 34,000 34,001 40,000 40,001 48,000 48,001 60,000 48,001 60,000 60, ,000 60, ,000 USA United States Dollar USD 15,000 15,001 25,000 60,001 70,000 70,001 85, , , , , , ,000 Vietnam Vietnamese Dong VND,000 10,000,001 20,000,000 20,000,001 50,000,000 20,000,001 50,000,000 50,000,001 90,000,000 90,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000, ,000,001 In some countries, the same income bracket will appear in two segments; in the US, for example, those with an average income of 25,000-32,000 USD appear in both the Bottom 25% and the Mid 50%. To determine how to allocate respondents who appear in these crossover brackets, we assign them wealth scores which factor in responses to other questions in the survey (such as value of savings and investments, car & house ownership, etc). For example, if two respondents had both selected 25,000-32,000 USD as their income, but one had indicated that they had savings, a car and that they owned property, and the other had answered negatively to these questions, the respondent with the savings & other assets would be assigned to the middle 50% and the one without would be placed in the bottom 25%. 6